Black-box optimization benchmarking of IPOP-saACM-ES on the BBOB-2012 noisy testbed

Ilya Loshchilov 1 Marc Schoenauer 1, 2 Michèle Sebag 1, 3
1 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
2 MSR - INRIA - Microsoft Research - Inria Joint Centre
3 LRI - Laboratoire de Recherche en Informatique
Abstract : In this paper, we study the performance of IPOP-saACM-ES, recently proposed self-adaptive surrogate-assisted Covariance Matrix Adaptation Evolution Strategy. The algorithm was tested using restarts till a total number of function evaluations of 10^6D was reached, where D is the dimension of the function search space. The experiments show that the surrogate model control allows IPOP-saACM-ES to be as robust as the original IPOP-aCMA-ES and outperforms the latter by a factor from 2 to 3 on 6 benchmark problems with moderate noise. On 15 out of 30 benchmark problems in dimension 20, IPOP-saACM-ES exceeds the records observed during BBOB-2009 and BBOB-2010.
Keywords : Benchmarking black-box optimization evolution strategy CMA-ES self-adaptation surrogate models ranking support vector machine surrogate-assisted optimization
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Conference papers
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Submitted on : Monday, April 23, 2012 - 6:05:49 PM
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  • HAL Id : hal-00690543, version 1
  • ARXIV : 1206.0974

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Ilya Loshchilov, Marc Schoenauer, Michèle Sebag. Black-box optimization benchmarking of IPOP-saACM-ES on the BBOB-2012 noisy testbed. Workshop Proceedings of the (GECCO) Genetic and Evolutionary Computation Conference, Jul 2012, Philadelphia, United States. ⟨hal-00690543⟩

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